Foreword

Driver response to congestion and road pricing is an essential element to forecasting the future use of roadway systems and estimating the effect that pricing has on demand and route choice. Though many studies have been conducted in the past and revenue studies are routinely done for proposed toll roads, there is still a need for improving the behavioral basis for forecast. The objective of this project was to develop mathematical descriptions of the full range of highway user behavioral responses to congestion, travel time reliability, and pricing. These descriptions were achieved by mining existing data sets. The report estimates a series of nine utility equations, progressively adding variables of interest. This research explores the effect on demand and route choice of demographic characteristics, car occupancy, value of travel time, value of travel time reliability, situational variability, and an observed toll aversion bias. The primary audience for this research is professionals who develop travel demand and traffic forecasts. Policy makers may also have an interest in the behavioral findings that could have policy implications. Equations for commercial drivers were not developed since their routes are normally determined, in part, by contracts and company policies.

The researchers for this study identified both revealed and stated preference data sets that could be mined to estimate equations on driver responses to congestion and tolls. The primary data sets were from Seattle and New York. Supporting data sets, used for testing transferability of the equations, included San Francisco, Minneapolis, Chicago, San Diego, Orange County (CA), and Baltimore. A hierarchical choice framework was used. The choice framework considers first residential location and activities, followed by primary destination and intermediate stops, mode of travel, occupancy (when applicable), time of day, departure window, and finally route choice.

The basic utility equation features travel time and cost with coefficients estimated from the data sets. Additional levels of disaggregation may be used depending on the availability of data. In the next level, the equation specifies time to mean “free flow” and “congested” time. The data analysis indicates that drivers perceive every minute driving in congested conditions at 1.5 to 2.0 times longer than free flow travel time. In the next level, which adjusts the cost term for income, research shows that the value of travel time increases with income, but not linearly. The cost term is subsequently disaggregated by auto occupancy, followed by personal characteristics such as trip purpose, age, and gender. Sensitivity test­ing shows that segmentation by trip purpose is significant, but other personal character­istics are not extremely significant. Travel time reliability, considered in the next level, is the standard deviation of travel time adjusted for distance. This equation recognizes that the value of travel time reliability for short trips (e.g., 5 miles), especially trips to and from work, is greater. The next variable revealed from the data is a toll aversion bias, represent­ing a psychological perception over and above time-cost trade-offs. The toll aversion bias is equivalent to 15–20 minutes of travel time even in areas with a long history of toll roads. The final term in the complete equation represents unobserved heterogeneity. This variable is significant because it represents what may be called “trip pressure” or other situational factors in which there is a penalty for lateness (e.g., trips to the airport or to pick up children). People making such trips are often willing to pay a toll rate higher than demographic or trip purpose characteristics would indicate.

This research reveals a number of policy implications. Drivers place a value on travel time across a wide range from $5 to over $50 per hour and approaching $100 per hour when trip pressure is high. Therefore, toll levels have to be significant to influence congestion. Travelers’ responses to congestion and pricing are also dependent on the options avail­able. Driver response to congestion and pricing usually escalates from changing a route or departure time, to switching to transit if available, to rescheduling trips, and finally moving or changing jobs. Providing travel options is an important complement to a road pricing strategy that is aimed at reducing congestion. Finally, improvements to travel time reliability are as important as improvements to average travel time. This implies that operational improvements and information provided to travelers may be as valuable as increases in speed.

The report contains extensive documentation on the estimation of these models and the policy implications. It also contains insights on the value of travel time reliability and the use of reliability in travel demand and simulation models.